{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gluon CIFAR-10 Hyperparameter Tuning\n",
    "_**ResNet model in Gluon trained with SageMaker Automatic Model Tuning and Random Search Tuning**_\n",
    "\n",
    "---\n",
    "\n",
    "---\n",
    "\n",
    "_This notebook was created and tested on an ml.m4.xlarge notebook instance.  However, the tuning jobs use multiple ml.p3.8xlarge instances, meaning re-running this test could cost approximately \\$400.  Please do not use Cell -> Run All.  Certain cell outputs have not been cleared so that you can see results without having to run the notebook yourself._\n",
    "\n",
    "## Outline\n",
    "\n",
    "1. [Background](#Background)\n",
    "1. [Setup](#Setup)\n",
    "1. [Data](#Data)\n",
    "1. [Script](#Script)\n",
    "1. [Train: Initial](#Train:-Initial)\n",
    "1. [Tune: Random](#Tune:-Random)\n",
    "1. [Tune: Automatic Model Tuning](#Tune:-Automatic-Model-Tuning)\n",
    "1. [Wrap-up](#Wrap-up)\n",
    "\n",
    "## Background\n",
    "\n",
    "Selecting the right hyperparameter values for your machine learning model can be difficult.  The right answer is dependent on your data; some algorithms have many different hyperparameters that can be tweaked; some are very sensitive to the hyperparameter values selected; and most have a non-linear relationship between model fit and hyperparameter values.\n",
    "\n",
    "There are a variety of strategies to select hyperparameter values.  Some scientists use domain knowledge, heuristics, intuition, or manual experimentation; others use brute force searches; and some build meta models to predict what performant hyperparameter values may be.  But regardless of the method, it usually requires a specialized skill set.  Meanwhile, most scientists themselves would prefer to be creating new models rather than endlessly refining an old one.\n",
    "\n",
    "Amazon SageMaker can ease this process with Automatic Model Tuning.  This technique uses Gaussian Process regression to predict which hyperparameter values may be most effective at improving fit, and Bayesian optimization to balance exploring the hyperparameter space (so that a better predictive model for hyperparameters can be built) and exploiting specific hyperparameter values when needed.\n",
    "\n",
    "Other popular methods of hyperparameter optimization include brute force methods like random search.  Despite sounding naive, this is often very competitive.  However, we've found SageMaker's Automatic Model Tuning to provide better fits in fewer job runs, resulting in a better model with less time spent and at a lower cost.  This notebook will compare the two methods in more detail.\n",
    "\n",
    "SageMaker's Automatic Model Tuning works with SageMaker's built-in algorithms, pre-built deep learning frameworks, and the bring your own algorithm container options.  But, for this example, let's stick with the MXNet framework, a ResNet-34 convolutional neural network, and the [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) image dataset.  For more background, please see the [MXNet CIFAR-10 example notebook](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_gluon_cifar10/mxnet_cifar10_with_gluon.ipynb).\n",
    "\n",
    "## Setup\n",
    "\n",
    "Specify the IAM role for permission to access the dataset in S3 and SageMaker functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "\n",
    "sagemaker_session = sagemaker.Session()\n",
    "role = sagemaker.get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.mxnet import MXNet\n",
    "from sagemaker.tuner import IntegerParameter, CategoricalParameter, ContinuousParameter, HyperparameterTuner\n",
    "import random_tuner as rt\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Data\n",
    "\n",
    "We'll use a helper script to download [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) training data and sample images.  CIFAR-10 consists of 60K 32x32 pixel color images (50K train, 10K test) evenly distributed across 10 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cifar10_utils import download_training_data\n",
    "download_training_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll use the `sagemaker.Session.upload_data` function to upload our datasets to an S3 location. The return value `inputs` identifies the location -- we will use this later when we start the training and tuning jobs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = sagemaker_session.upload_data(path='data', key_prefix='data/DEMO-gluon-cifar10')\n",
    "print('input spec (in this case, just an S3 path): {}'.format(inputs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Script\n",
    "\n",
    "We need to provide a training script that can run on the SageMaker platform. This is idiomatic MXNet code arranged into a few key functions:\n",
    "* A `train()` function that takes in hyperparameters, defines our neural net architecture, and trains our network.\n",
    "* A `save()` function that saves our trained network as an MXNet model.\n",
    "* Helper functions `get_data()`, `get_train_data()`, and `get_test_data()` which prepare the CIFAR-10 image data for our `train()` function.\n",
    "* A helper function called `test()` which calculates our accuracy on the holdout datasets.\n",
    "* Hosting functions (which we keep for alignment with other MXNet CIFAR-10 notebooks, but won't dig into since the focus of this notebook is only on training).\n",
    "\n",
    "The network itself is a ResNet-34 architecture imported from the [Gluon Model Zoo](https://mxnet.incubator.apache.org/versions/master/api/python/gluon/model_zoo.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cat 'cifar10.py'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Train: Initial\n",
    "\n",
    "Now that we've written our training script, we can submit it as a job to SageMaker training.  Normally, we might test locally to ensure our script worked (See the [MXNet CIFAR-10 local mode example](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_gluon_cifar10/mxnet_cifar10_local_mode.ipynb)), but since we already know the script works, we'll skip that step.\n",
    "\n",
    "Let's see how our model performs with a naive guess for hyperparameter values.  We're training our network with stochastic gradient descent (SGD), which is an iterative method to minimize our training loss by finding the direction to change our network weights that improves training loss and then taking a small step in that direction and repeating.  Since we're using SGD, the three hyperparameters we'll focus on will be:\n",
    "\n",
    "* `learning_rate`: which controls how large of steps we take.\n",
    "* `momentum`: which uses information from the direction of our previous step to inform our current step.\n",
    "* `wd`: which penalizes weights when they grow too large.\n",
    "\n",
    "In this case, we'll set the hyperparameters to MXNet's default values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = MXNet('cifar10.py',\n",
    "          role=role,\n",
    "          train_instance_count=1,\n",
    "          train_instance_type='ml.p3.8xlarge',\n",
    "          framework_version='1.4.0',\n",
    "          py_version='py3',\n",
    "          hyperparameters={'batch_size': 1024,\n",
    "                           'epochs': 50,\n",
    "                           'learning_rate': 0.01,\n",
    "                           'momentum': 0.,\n",
    "                           'wd': 0.})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've constructed our `MXNet` object, we can fit it using the data we uploaded to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m.fit(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, our accuracy is only about 53% on our validation dataset.  CIFAR-10 can be challenging, but we'd want our accuracy much better than just over half if users are depending on an accurate prediction.\n",
    "\n",
    "---\n",
    "\n",
    "## Tune: Random\n",
    "\n",
    "One method of hyperparameter tuning that performs surprisingly well for how simple it is, is randomly trying a variety of hyperparameter values within set ranges.  So, for this example, we've created a helper script `random_tuner.py` to help us do this.\n",
    "\n",
    "We'll need to supply:\n",
    "\n",
    "* A function that trains our MXNet model given a job name and list of hyperparameters.  Note, `wait` is set to false in our `fit()` call so that we can train multiple jobs at once.\n",
    "* A dictionary of hyperparameters where the ones we want to tune are defined as one of three types (`ContinuousParameter`, `IntegerParameter`, or `CategoricalParameter`) and appropriate minimum and maximum ranges or a list of possible values are provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_random(job_name, hyperparameters):\n",
    "    m = MXNet('cifar10.py',\n",
    "              role=role,\n",
    "              train_instance_count=1,\n",
    "              train_instance_type='ml.p3.8xlarge',\n",
    "              framework_version='1.4.0',\n",
    "              py_version='py3',\n",
    "              hyperparameters=hyperparameters)\n",
    "    m.fit(inputs, wait=False, job_name=job_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameters = {'batch_size': 1024,\n",
    "                   'epochs': 50,\n",
    "                   'learning_rate': rt.ContinuousParameter(0.001, 0.5),\n",
    "                   'momentum': rt.ContinuousParameter(0., 0.99),\n",
    "                   'wd': rt.ContinuousParameter(0., 0.001)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can kick off our random search.  We've defined the total number of training jobs to be 120.  This is a large amount and drives most of the cost of this notebook.  Also, we've specified up to 8 jobs to be run in parallel.  This exceeds the default concurrent instance limit for ml.p3.8xlarge instances.  If you're just testing this notebook out, decreasing both values will control costs and allow you to complete successfully without requiring a service limit increase.\n",
    "\n",
    "_Note, this step may take up to 2 hours to complete.  Even if you loose connection with the notebook in the middle, as long as the notebook instance continues to run, `jobs` should still be successfully created for future use._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "jobs = rt.random_search(fit_random,\n",
    "                        hyperparameters,\n",
    "                        max_jobs=120,\n",
    "                        max_parallel_jobs=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once our random search completes, we'll want to compare our training jobs (which may take a few extra minutes to finish) in order to understand how our objective metric (% accuracy on our validation dataset) varies by hyperparameter values.  In this case, our helper function includes two functions.\n",
    "\n",
    "* `get_metrics()` scrapes the CloudWatch logs for our training jobs and uses a regex to return any reported values of our objective metric.\n",
    "* `table_metrics()` joins on the hyperparameter values for each job, grabs the ending objective value, and converts the result to a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "      <td>0.726074</td>\n",
       "      <td>0.266806</td>\n",
       "      <td>10</td>\n",
       "      <td>0.671921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-79</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.726074</td>\n",
       "      <td>0.204075</td>\n",
       "      <td>79</td>\n",
       "      <td>0.921536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-66</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.724731</td>\n",
       "      <td>0.269668</td>\n",
       "      <td>66</td>\n",
       "      <td>0.951994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-21</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.724487</td>\n",
       "      <td>0.262985</td>\n",
       "      <td>21</td>\n",
       "      <td>0.726774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-75</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.724243</td>\n",
       "      <td>0.239251</td>\n",
       "      <td>75</td>\n",
       "      <td>0.336703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-85</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.723267</td>\n",
       "      <td>0.140760</td>\n",
       "      <td>85</td>\n",
       "      <td>0.790066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-105</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.723145</td>\n",
       "      <td>0.276357</td>\n",
       "      <td>105</td>\n",
       "      <td>0.608669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-65</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.722900</td>\n",
       "      <td>0.147761</td>\n",
       "      <td>65</td>\n",
       "      <td>0.745701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-96</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.676025</td>\n",
       "      <td>0.142288</td>\n",
       "      <td>96</td>\n",
       "      <td>0.153832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-0</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.671997</td>\n",
       "      <td>0.030509</td>\n",
       "      <td>0</td>\n",
       "      <td>0.861382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-112</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.671997</td>\n",
       "      <td>0.135736</td>\n",
       "      <td>112</td>\n",
       "      <td>0.232466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-103</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.671265</td>\n",
       "      <td>0.094724</td>\n",
       "      <td>103</td>\n",
       "      <td>0.477036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-27</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.670898</td>\n",
       "      <td>0.051749</td>\n",
       "      <td>27</td>\n",
       "      <td>0.619780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-53</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.667236</td>\n",
       "      <td>0.064741</td>\n",
       "      <td>53</td>\n",
       "      <td>0.519927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-39</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000864</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.665771</td>\n",
       "      <td>0.147716</td>\n",
       "      <td>39</td>\n",
       "      <td>0.013635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-8</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.665649</td>\n",
       "      <td>0.093428</td>\n",
       "      <td>8</td>\n",
       "      <td>0.490986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-116</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.660278</td>\n",
       "      <td>0.099005</td>\n",
       "      <td>116</td>\n",
       "      <td>0.364318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-62</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.658936</td>\n",
       "      <td>0.114799</td>\n",
       "      <td>62</td>\n",
       "      <td>0.237339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-54</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.658569</td>\n",
       "      <td>0.117171</td>\n",
       "      <td>54</td>\n",
       "      <td>0.121602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-52</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.658447</td>\n",
       "      <td>0.074876</td>\n",
       "      <td>52</td>\n",
       "      <td>0.431588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-69</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.656128</td>\n",
       "      <td>0.122641</td>\n",
       "      <td>69</td>\n",
       "      <td>0.118055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-59</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.653442</td>\n",
       "      <td>0.116648</td>\n",
       "      <td>59</td>\n",
       "      <td>0.006298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-48</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.652466</td>\n",
       "      <td>0.048692</td>\n",
       "      <td>48</td>\n",
       "      <td>0.516305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-100</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.641968</td>\n",
       "      <td>0.473007</td>\n",
       "      <td>100</td>\n",
       "      <td>0.063985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-64</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.640137</td>\n",
       "      <td>0.465702</td>\n",
       "      <td>64</td>\n",
       "      <td>0.928012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-86</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.638550</td>\n",
       "      <td>0.077085</td>\n",
       "      <td>86</td>\n",
       "      <td>0.130483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-82</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.638428</td>\n",
       "      <td>0.073119</td>\n",
       "      <td>82</td>\n",
       "      <td>0.193865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-9</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.629272</td>\n",
       "      <td>0.049321</td>\n",
       "      <td>9</td>\n",
       "      <td>0.335158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-58</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.617188</td>\n",
       "      <td>0.027019</td>\n",
       "      <td>58</td>\n",
       "      <td>0.584695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-12</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.615845</td>\n",
       "      <td>0.045045</td>\n",
       "      <td>12</td>\n",
       "      <td>0.261383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-7</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.606812</td>\n",
       "      <td>0.033248</td>\n",
       "      <td>7</td>\n",
       "      <td>0.465235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-93</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.605591</td>\n",
       "      <td>0.043833</td>\n",
       "      <td>93</td>\n",
       "      <td>0.199631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-95</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.568848</td>\n",
       "      <td>0.380469</td>\n",
       "      <td>95</td>\n",
       "      <td>0.948769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-60</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.557129</td>\n",
       "      <td>0.018272</td>\n",
       "      <td>60</td>\n",
       "      <td>0.184786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-84</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.530518</td>\n",
       "      <td>0.005893</td>\n",
       "      <td>84</td>\n",
       "      <td>0.299194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-68</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.511475</td>\n",
       "      <td>0.309750</td>\n",
       "      <td>68</td>\n",
       "      <td>0.968020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-83</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.233398</td>\n",
       "      <td>0.466878</td>\n",
       "      <td>83</td>\n",
       "      <td>0.152383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random-hp-2018-07-08-20-06-10-189-99</th>\n",
       "      <td>50</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>1024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.310720</td>\n",
       "      <td>99</td>\n",
       "      <td>0.817482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       epochs        wd  batch_size  \\\n",
       "random-hp-2018-07-08-20-06-10-189-17       50  0.000539        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-106      50  0.000658        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-78       50  0.000955        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-15       50  0.000304        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-16       50  0.000849        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-117      50  0.000708        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-70       50  0.000115        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-28       50  0.000593        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-2        50  0.000155        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-81       50  0.000810        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-104      50  0.000001        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-91       50  0.000324        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-80       50  0.000307        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-4        50  0.000478        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-20       50  0.000526        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-25       50  0.000636        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-40       50  0.000565        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-44       50  0.000305        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-77       50  0.000308        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-92       50  0.000830        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-101      50  0.000999        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-51       50  0.000371        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-10       50  0.000663        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-79       50  0.000176        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-66       50  0.000502        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-21       50  0.000166        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-75       50  0.000609        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-85       50  0.000403        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-105      50  0.000472        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-65       50  0.000160        1024   \n",
       "...                                       ...       ...         ...   \n",
       "random-hp-2018-07-08-20-06-10-189-96       50  0.000215        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-0        50  0.000354        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-112      50  0.000010        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-103      50  0.000895        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-27       50  0.000418        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-53       50  0.000646        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-39       50  0.000864        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-8        50  0.000274        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-116      50  0.000075        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-62       50  0.000040        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-54       50  0.000137        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-52       50  0.000580        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-69       50  0.000094        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-59       50  0.000820        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-48       50  0.000049        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-100      50  0.000995        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-64       50  0.000209        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-86       50  0.000641        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-82       50  0.000761        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-9        50  0.000321        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-58       50  0.000147        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-12       50  0.000417        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-7        50  0.000642        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-93       50  0.000527        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-95       50  0.000005        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-60       50  0.000769        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-84       50  0.000446        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-68       50  0.000088        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-83       50  0.000377        1024   \n",
       "random-hp-2018-07-08-20-06-10-189-99       50  0.000059        1024   \n",
       "\n",
       "                                       objective  learning_rate  job_number  \\\n",
       "random-hp-2018-07-08-20-06-10-189-17    0.736938       0.346114          17   \n",
       "random-hp-2018-07-08-20-06-10-189-106   0.736572       0.203518         106   \n",
       "random-hp-2018-07-08-20-06-10-189-78    0.735352       0.044036          78   \n",
       "random-hp-2018-07-08-20-06-10-189-15    0.733887       0.187376          15   \n",
       "random-hp-2018-07-08-20-06-10-189-16    0.733643       0.381012          16   \n",
       "random-hp-2018-07-08-20-06-10-189-117   0.732544       0.314118         117   \n",
       "random-hp-2018-07-08-20-06-10-189-70    0.732178       0.396326          70   \n",
       "random-hp-2018-07-08-20-06-10-189-28    0.731689       0.398318          28   \n",
       "random-hp-2018-07-08-20-06-10-189-2     0.731689       0.144946           2   \n",
       "random-hp-2018-07-08-20-06-10-189-81    0.730103       0.351694          81   \n",
       "random-hp-2018-07-08-20-06-10-189-104   0.729980       0.450279         104   \n",
       "random-hp-2018-07-08-20-06-10-189-91    0.729980       0.289774          91   \n",
       "random-hp-2018-07-08-20-06-10-189-80    0.729858       0.242544          80   \n",
       "random-hp-2018-07-08-20-06-10-189-4     0.729736       0.158774           4   \n",
       "random-hp-2018-07-08-20-06-10-189-20    0.729736       0.394191          20   \n",
       "random-hp-2018-07-08-20-06-10-189-25    0.729126       0.293751          25   \n",
       "random-hp-2018-07-08-20-06-10-189-40    0.728271       0.150810          40   \n",
       "random-hp-2018-07-08-20-06-10-189-44    0.728271       0.222650          44   \n",
       "random-hp-2018-07-08-20-06-10-189-77    0.727783       0.412024          77   \n",
       "random-hp-2018-07-08-20-06-10-189-92    0.727295       0.401129          92   \n",
       "random-hp-2018-07-08-20-06-10-189-101   0.726807       0.299126         101   \n",
       "random-hp-2018-07-08-20-06-10-189-51    0.726074       0.413220          51   \n",
       "random-hp-2018-07-08-20-06-10-189-10    0.726074       0.266806          10   \n",
       "random-hp-2018-07-08-20-06-10-189-79    0.726074       0.204075          79   \n",
       "random-hp-2018-07-08-20-06-10-189-66    0.724731       0.269668          66   \n",
       "random-hp-2018-07-08-20-06-10-189-21    0.724487       0.262985          21   \n",
       "random-hp-2018-07-08-20-06-10-189-75    0.724243       0.239251          75   \n",
       "random-hp-2018-07-08-20-06-10-189-85    0.723267       0.140760          85   \n",
       "random-hp-2018-07-08-20-06-10-189-105   0.723145       0.276357         105   \n",
       "random-hp-2018-07-08-20-06-10-189-65    0.722900       0.147761          65   \n",
       "...                                          ...            ...         ...   \n",
       "random-hp-2018-07-08-20-06-10-189-96    0.676025       0.142288          96   \n",
       "random-hp-2018-07-08-20-06-10-189-0     0.671997       0.030509           0   \n",
       "random-hp-2018-07-08-20-06-10-189-112   0.671997       0.135736         112   \n",
       "random-hp-2018-07-08-20-06-10-189-103   0.671265       0.094724         103   \n",
       "random-hp-2018-07-08-20-06-10-189-27    0.670898       0.051749          27   \n",
       "random-hp-2018-07-08-20-06-10-189-53    0.667236       0.064741          53   \n",
       "random-hp-2018-07-08-20-06-10-189-39    0.665771       0.147716          39   \n",
       "random-hp-2018-07-08-20-06-10-189-8     0.665649       0.093428           8   \n",
       "random-hp-2018-07-08-20-06-10-189-116   0.660278       0.099005         116   \n",
       "random-hp-2018-07-08-20-06-10-189-62    0.658936       0.114799          62   \n",
       "random-hp-2018-07-08-20-06-10-189-54    0.658569       0.117171          54   \n",
       "random-hp-2018-07-08-20-06-10-189-52    0.658447       0.074876          52   \n",
       "random-hp-2018-07-08-20-06-10-189-69    0.656128       0.122641          69   \n",
       "random-hp-2018-07-08-20-06-10-189-59    0.653442       0.116648          59   \n",
       "random-hp-2018-07-08-20-06-10-189-48    0.652466       0.048692          48   \n",
       "random-hp-2018-07-08-20-06-10-189-100   0.641968       0.473007         100   \n",
       "random-hp-2018-07-08-20-06-10-189-64    0.640137       0.465702          64   \n",
       "random-hp-2018-07-08-20-06-10-189-86    0.638550       0.077085          86   \n",
       "random-hp-2018-07-08-20-06-10-189-82    0.638428       0.073119          82   \n",
       "random-hp-2018-07-08-20-06-10-189-9     0.629272       0.049321           9   \n",
       "random-hp-2018-07-08-20-06-10-189-58    0.617188       0.027019          58   \n",
       "random-hp-2018-07-08-20-06-10-189-12    0.615845       0.045045          12   \n",
       "random-hp-2018-07-08-20-06-10-189-7     0.606812       0.033248           7   \n",
       "random-hp-2018-07-08-20-06-10-189-93    0.605591       0.043833          93   \n",
       "random-hp-2018-07-08-20-06-10-189-95    0.568848       0.380469          95   \n",
       "random-hp-2018-07-08-20-06-10-189-60    0.557129       0.018272          60   \n",
       "random-hp-2018-07-08-20-06-10-189-84    0.530518       0.005893          84   \n",
       "random-hp-2018-07-08-20-06-10-189-68    0.511475       0.309750          68   \n",
       "random-hp-2018-07-08-20-06-10-189-83    0.233398       0.466878          83   \n",
       "random-hp-2018-07-08-20-06-10-189-99         NaN       0.310720          99   \n",
       "\n",
       "                                       momentum  \n",
       "random-hp-2018-07-08-20-06-10-189-17   0.231219  \n",
       "random-hp-2018-07-08-20-06-10-189-106  0.808102  \n",
       "random-hp-2018-07-08-20-06-10-189-78   0.962561  \n",
       "random-hp-2018-07-08-20-06-10-189-15   0.954231  \n",
       "random-hp-2018-07-08-20-06-10-189-16   0.049903  \n",
       "random-hp-2018-07-08-20-06-10-189-117  0.817854  \n",
       "random-hp-2018-07-08-20-06-10-189-70   0.510912  \n",
       "random-hp-2018-07-08-20-06-10-189-28   0.394819  \n",
       "random-hp-2018-07-08-20-06-10-189-2    0.924371  \n",
       "random-hp-2018-07-08-20-06-10-189-81   0.731069  \n",
       "random-hp-2018-07-08-20-06-10-189-104  0.100731  \n",
       "random-hp-2018-07-08-20-06-10-189-91   0.705745  \n",
       "random-hp-2018-07-08-20-06-10-189-80   0.544332  \n",
       "random-hp-2018-07-08-20-06-10-189-4    0.793798  \n",
       "random-hp-2018-07-08-20-06-10-189-20   0.826365  \n",
       "random-hp-2018-07-08-20-06-10-189-25   0.304069  \n",
       "random-hp-2018-07-08-20-06-10-189-40   0.795445  \n",
       "random-hp-2018-07-08-20-06-10-189-44   0.744344  \n",
       "random-hp-2018-07-08-20-06-10-189-77   0.049680  \n",
       "random-hp-2018-07-08-20-06-10-189-92   0.514144  \n",
       "random-hp-2018-07-08-20-06-10-189-101  0.545094  \n",
       "random-hp-2018-07-08-20-06-10-189-51   0.472064  \n",
       "random-hp-2018-07-08-20-06-10-189-10   0.671921  \n",
       "random-hp-2018-07-08-20-06-10-189-79   0.921536  \n",
       "random-hp-2018-07-08-20-06-10-189-66   0.951994  \n",
       "random-hp-2018-07-08-20-06-10-189-21   0.726774  \n",
       "random-hp-2018-07-08-20-06-10-189-75   0.336703  \n",
       "random-hp-2018-07-08-20-06-10-189-85   0.790066  \n",
       "random-hp-2018-07-08-20-06-10-189-105  0.608669  \n",
       "random-hp-2018-07-08-20-06-10-189-65   0.745701  \n",
       "...                                         ...  \n",
       "random-hp-2018-07-08-20-06-10-189-96   0.153832  \n",
       "random-hp-2018-07-08-20-06-10-189-0    0.861382  \n",
       "random-hp-2018-07-08-20-06-10-189-112  0.232466  \n",
       "random-hp-2018-07-08-20-06-10-189-103  0.477036  \n",
       "random-hp-2018-07-08-20-06-10-189-27   0.619780  \n",
       "random-hp-2018-07-08-20-06-10-189-53   0.519927  \n",
       "random-hp-2018-07-08-20-06-10-189-39   0.013635  \n",
       "random-hp-2018-07-08-20-06-10-189-8    0.490986  \n",
       "random-hp-2018-07-08-20-06-10-189-116  0.364318  \n",
       "random-hp-2018-07-08-20-06-10-189-62   0.237339  \n",
       "random-hp-2018-07-08-20-06-10-189-54   0.121602  \n",
       "random-hp-2018-07-08-20-06-10-189-52   0.431588  \n",
       "random-hp-2018-07-08-20-06-10-189-69   0.118055  \n",
       "random-hp-2018-07-08-20-06-10-189-59   0.006298  \n",
       "random-hp-2018-07-08-20-06-10-189-48   0.516305  \n",
       "random-hp-2018-07-08-20-06-10-189-100  0.063985  \n",
       "random-hp-2018-07-08-20-06-10-189-64   0.928012  \n",
       "random-hp-2018-07-08-20-06-10-189-86   0.130483  \n",
       "random-hp-2018-07-08-20-06-10-189-82   0.193865  \n",
       "random-hp-2018-07-08-20-06-10-189-9    0.335158  \n",
       "random-hp-2018-07-08-20-06-10-189-58   0.584695  \n",
       "random-hp-2018-07-08-20-06-10-189-12   0.261383  \n",
       "random-hp-2018-07-08-20-06-10-189-7    0.465235  \n",
       "random-hp-2018-07-08-20-06-10-189-93   0.199631  \n",
       "random-hp-2018-07-08-20-06-10-189-95   0.948769  \n",
       "random-hp-2018-07-08-20-06-10-189-60   0.184786  \n",
       "random-hp-2018-07-08-20-06-10-189-84   0.299194  \n",
       "random-hp-2018-07-08-20-06-10-189-68   0.968020  \n",
       "random-hp-2018-07-08-20-06-10-189-83   0.152383  \n",
       "random-hp-2018-07-08-20-06-10-189-99   0.817482  \n",
       "\n",
       "[120 rows x 7 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_metrics = rt.table_metrics(jobs, rt.get_metrics(jobs, 'validation: accuracy=([0-9\\\\.]+)'))\n",
    "random_metrics.sort_values(['objective'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there's a huge variation in percent accuracy.  Had we initially (unknowingly) set our learning rate near 0.5, momentum at 0.15, and weight decay to 0.0004, we would have an accuracy just over 20% (this is particularly bad considering random guessing would produce 10% accuracy).\n",
    "\n",
    "But, we also found many successful hyperparameter value combinations, and reached a peak validation accuracy of 73.7%.  Note, this peak job occurs relatively early in our search but, due to randomness, our next best objective value occurred 89 jobs later.  The actual peak could have occurred anywhere within the 120 jobs and will change across multiple runs.  We can see that with hyperparameter tuning our accuracy is well above the default value baseline of 53%.\n",
    "\n",
    "To get a rough understanding of how the hyperparameter values relate to one another and the objective metric, let's quickly plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(random_metrics[['objective',\n",
    "                                           'learning_rate',\n",
    "                                           'momentum',\n",
    "                                           'wd',\n",
    "                                           'job_number']],\n",
    "                           figsize=(12, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hyperparameter's correlation with themselves and over time is essentially non-existent (which makes sense because we selected their values randomly).  However, in general, we notice:\n",
    "\n",
    "* Very low `learning_rate`s tend to do worse, although too high seems to add variability.\n",
    "* `momentum` seems to have less impact, with potentially a non-linear sweet spot near 0.8.\n",
    "* `wd` has a less consistent impact on accuracy than the other two hyperparameters.\n",
    "\n",
    "---\n",
    "\n",
    "## Tune: Automatic Model Tuning\n",
    "\n",
    "Now, let's try using Amazon SageMaker's Automatic Model Tuning.  Rather than selecting hyperparameter values randomly, SageMaker builds a second machine learning model which, based on previous hyperparameter and objective metric values, predicts new values that might yield an improvement.  This should allow us to train better models, faster and cheaper.\n",
    "\n",
    "We'll use the tuner functionality already built-in to the SageMaker Python SDK.  Let's start by defining a new `MXNet` estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt = MXNet('cifar10.py',\n",
    "           role=role,\n",
    "           train_instance_count=1,\n",
    "           train_instance_type='ml.p3.8xlarge',\n",
    "           framework_version='1.4.0',\n",
    "           py_version='py3',\n",
    "           hyperparameters={'batch_size': 1024,\n",
    "                            'epochs': 50})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can define our ranges (these take the same arguments as the classes from `random_tuner`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparameter_ranges = {'learning_rate': ContinuousParameter(0.001, 0.5),\n",
    "                         'momentum': ContinuousParameter(0., 0.99),\n",
    "                         'wd': ContinuousParameter(0., 0.001)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll define our objective metric and provide the regex needed to scrape it from our training jobs' CloudWatch logs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_metric_name = 'Validation-accuracy'\n",
    "metric_definitions = [{'Name': 'Validation-accuracy',\n",
    "                       'Regex': 'validation: accuracy=([0-9\\\\.]+)'}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a `HyperparameterTuner` object and fit it by pointing to our data in S3.  This kicks our tuning job off in the background.\n",
    "\n",
    "Notice, we specify a much smaller number of total jobs, and a smaller number of parallel jobs.  Since our model uses previous training job runs to predict where to test next, we get better results (although it takes longer) when setting this to a smaller value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuner = HyperparameterTuner(mt,\n",
    "                            objective_metric_name,\n",
    "                            hyperparameter_ranges,\n",
    "                            metric_definitions,\n",
    "                            max_jobs=30,\n",
    "                            max_parallel_jobs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuner.fit(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_You will be unable to successfully run the following cells until the tuning job completes.  This step may take up to 2 hours._\n",
    "\n",
    "Once the tuning job finishes, we can bring in a table of metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FinalObjectiveValue</th>\n",
       "      <th>TrainingElapsedTimeSeconds</th>\n",
       "      <th>TrainingEndTime</th>\n",
       "      <th>TrainingJobName</th>\n",
       "      <th>TrainingJobStatus</th>\n",
       "      <th>TrainingStartTime</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>momentum</th>\n",
       "      <th>wd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.739868</td>\n",
       "      <td>449.0</td>\n",
       "      <td>2018-07-08 06:32:39+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-018-0672e23c</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:25:10+00:00</td>\n",
       "      <td>0.218998</td>\n",
       "      <td>7.807712e-01</td>\n",
       "      <td>0.000463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.735352</td>\n",
       "      <td>484.0</td>\n",
       "      <td>2018-07-08 06:53:18+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-022-602fe997</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:45:14+00:00</td>\n",
       "      <td>0.491950</td>\n",
       "      <td>2.074798e-02</td>\n",
       "      <td>0.000330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.734741</td>\n",
       "      <td>527.0</td>\n",
       "      <td>2018-07-08 06:42:58+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-019-510b97e9</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:34:11+00:00</td>\n",
       "      <td>0.495178</td>\n",
       "      <td>1.530975e-02</td>\n",
       "      <td>0.000864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.732422</td>\n",
       "      <td>484.0</td>\n",
       "      <td>2018-07-08 06:22:48+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-016-3c3a8322</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:14:44+00:00</td>\n",
       "      <td>0.394037</td>\n",
       "      <td>7.685146e-01</td>\n",
       "      <td>0.000663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.731079</td>\n",
       "      <td>486.0</td>\n",
       "      <td>2018-07-08 07:03:57+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-023-feac622d</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:55:51+00:00</td>\n",
       "      <td>0.443138</td>\n",
       "      <td>6.272739e-01</td>\n",
       "      <td>0.000891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.730591</td>\n",
       "      <td>423.0</td>\n",
       "      <td>2018-07-08 07:35:16+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-030-b96d5377</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 07:28:13+00:00</td>\n",
       "      <td>0.095172</td>\n",
       "      <td>9.210969e-01</td>\n",
       "      <td>0.000408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.729370</td>\n",
       "      <td>468.0</td>\n",
       "      <td>2018-07-08 07:35:32+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-029-25c7583d</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 07:27:44+00:00</td>\n",
       "      <td>0.400276</td>\n",
       "      <td>5.187911e-02</td>\n",
       "      <td>0.000631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.729248</td>\n",
       "      <td>485.0</td>\n",
       "      <td>2018-07-08 05:07:29+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-002-65adfb1f</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 04:59:24+00:00</td>\n",
       "      <td>0.308497</td>\n",
       "      <td>8.221134e-01</td>\n",
       "      <td>0.000838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.728027</td>\n",
       "      <td>471.0</td>\n",
       "      <td>2018-07-08 05:40:08+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-008-a1ecd004</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:32:17+00:00</td>\n",
       "      <td>0.202243</td>\n",
       "      <td>7.614848e-01</td>\n",
       "      <td>0.000937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.727173</td>\n",
       "      <td>490.0</td>\n",
       "      <td>2018-07-08 06:19:35+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-015-305bc5d4</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:11:25+00:00</td>\n",
       "      <td>0.399027</td>\n",
       "      <td>7.784146e-01</td>\n",
       "      <td>0.000653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.726196</td>\n",
       "      <td>427.0</td>\n",
       "      <td>2018-07-08 06:02:44+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-012-7dad3bd5</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:55:37+00:00</td>\n",
       "      <td>0.383464</td>\n",
       "      <td>1.314750e-02</td>\n",
       "      <td>0.000765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.725098</td>\n",
       "      <td>436.0</td>\n",
       "      <td>2018-07-08 06:12:13+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-014-031a27ff</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:04:57+00:00</td>\n",
       "      <td>0.499998</td>\n",
       "      <td>1.880845e-07</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.723877</td>\n",
       "      <td>508.0</td>\n",
       "      <td>2018-07-08 05:18:42+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-004-4cdcdee1</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:10:14+00:00</td>\n",
       "      <td>0.485280</td>\n",
       "      <td>8.946790e-01</td>\n",
       "      <td>0.000822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.720825</td>\n",
       "      <td>532.0</td>\n",
       "      <td>2018-07-08 05:29:59+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-006-8a90d72d</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:21:07+00:00</td>\n",
       "      <td>0.453483</td>\n",
       "      <td>6.834462e-01</td>\n",
       "      <td>0.000155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.719360</td>\n",
       "      <td>457.0</td>\n",
       "      <td>2018-07-08 06:42:14+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-020-8b0ac13f</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:34:37+00:00</td>\n",
       "      <td>0.408935</td>\n",
       "      <td>1.748506e-01</td>\n",
       "      <td>0.000529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.719238</td>\n",
       "      <td>426.0</td>\n",
       "      <td>2018-07-08 06:52:40+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-021-9752b883</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:45:34+00:00</td>\n",
       "      <td>0.323677</td>\n",
       "      <td>1.015553e-01</td>\n",
       "      <td>0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.717773</td>\n",
       "      <td>515.0</td>\n",
       "      <td>2018-07-08 06:30:54+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-017-40ce6e41</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:22:19+00:00</td>\n",
       "      <td>0.288515</td>\n",
       "      <td>8.637047e-01</td>\n",
       "      <td>0.000709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.716675</td>\n",
       "      <td>497.0</td>\n",
       "      <td>2018-07-08 07:25:28+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-028-0fd8fcda</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 07:17:11+00:00</td>\n",
       "      <td>0.210008</td>\n",
       "      <td>6.103743e-01</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.715942</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2018-07-08 07:25:56+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-027-99e8ad93</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 07:17:06+00:00</td>\n",
       "      <td>0.303875</td>\n",
       "      <td>8.004766e-01</td>\n",
       "      <td>0.000370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.714722</td>\n",
       "      <td>440.0</td>\n",
       "      <td>2018-07-08 06:08:50+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-013-9338d9e2</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:01:30+00:00</td>\n",
       "      <td>0.449889</td>\n",
       "      <td>1.005885e-01</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.712402</td>\n",
       "      <td>473.0</td>\n",
       "      <td>2018-07-08 05:39:19+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-007-8c6d6369</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:31:26+00:00</td>\n",
       "      <td>0.293129</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.709473</td>\n",
       "      <td>425.0</td>\n",
       "      <td>2018-07-08 07:13:34+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-026-d328bcb0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 07:06:29+00:00</td>\n",
       "      <td>0.347373</td>\n",
       "      <td>7.542925e-01</td>\n",
       "      <td>0.000085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.708130</td>\n",
       "      <td>504.0</td>\n",
       "      <td>2018-07-08 05:29:14+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-005-6644667e</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:20:50+00:00</td>\n",
       "      <td>0.472519</td>\n",
       "      <td>5.562326e-01</td>\n",
       "      <td>0.000195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.704956</td>\n",
       "      <td>493.0</td>\n",
       "      <td>2018-07-08 05:50:56+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-010-b43d406a</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:42:43+00:00</td>\n",
       "      <td>0.138131</td>\n",
       "      <td>6.934573e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.695679</td>\n",
       "      <td>468.0</td>\n",
       "      <td>2018-07-08 07:03:36+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-024-a1a9261a</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 06:55:48+00:00</td>\n",
       "      <td>0.460894</td>\n",
       "      <td>6.460432e-01</td>\n",
       "      <td>0.000909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.690430</td>\n",
       "      <td>516.0</td>\n",
       "      <td>2018-07-08 07:15:03+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-025-bd0557c9</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 07:06:27+00:00</td>\n",
       "      <td>0.392321</td>\n",
       "      <td>8.076493e-01</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.687378</td>\n",
       "      <td>522.0</td>\n",
       "      <td>2018-07-08 05:08:11+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-001-cb6d6b71</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 04:59:29+00:00</td>\n",
       "      <td>0.094162</td>\n",
       "      <td>5.240358e-01</td>\n",
       "      <td>0.000551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.658325</td>\n",
       "      <td>615.0</td>\n",
       "      <td>2018-07-08 05:52:55+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-009-f12240b7</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:42:40+00:00</td>\n",
       "      <td>0.285951</td>\n",
       "      <td>9.703842e-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.544189</td>\n",
       "      <td>478.0</td>\n",
       "      <td>2018-07-08 05:17:47+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-003-47aaa565</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:09:49+00:00</td>\n",
       "      <td>0.007274</td>\n",
       "      <td>3.568501e-01</td>\n",
       "      <td>0.000816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.417725</td>\n",
       "      <td>374.0</td>\n",
       "      <td>2018-07-08 05:59:31+00:00</td>\n",
       "      <td>sagemaker-mxnet-180708-0457-011-492642e3</td>\n",
       "      <td>Completed</td>\n",
       "      <td>2018-07-08 05:53:17+00:00</td>\n",
       "      <td>0.460787</td>\n",
       "      <td>9.900000e-01</td>\n",
       "      <td>0.000985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FinalObjectiveValue  TrainingElapsedTimeSeconds           TrainingEndTime  \\\n",
       "12             0.739868                       449.0 2018-07-08 06:32:39+00:00   \n",
       "8              0.735352                       484.0 2018-07-08 06:53:18+00:00   \n",
       "11             0.734741                       527.0 2018-07-08 06:42:58+00:00   \n",
       "14             0.732422                       484.0 2018-07-08 06:22:48+00:00   \n",
       "7              0.731079                       486.0 2018-07-08 07:03:57+00:00   \n",
       "0              0.730591                       423.0 2018-07-08 07:35:16+00:00   \n",
       "1              0.729370                       468.0 2018-07-08 07:35:32+00:00   \n",
       "28             0.729248                       485.0 2018-07-08 05:07:29+00:00   \n",
       "22             0.728027                       471.0 2018-07-08 05:40:08+00:00   \n",
       "15             0.727173                       490.0 2018-07-08 06:19:35+00:00   \n",
       "18             0.726196                       427.0 2018-07-08 06:02:44+00:00   \n",
       "16             0.725098                       436.0 2018-07-08 06:12:13+00:00   \n",
       "26             0.723877                       508.0 2018-07-08 05:18:42+00:00   \n",
       "24             0.720825                       532.0 2018-07-08 05:29:59+00:00   \n",
       "10             0.719360                       457.0 2018-07-08 06:42:14+00:00   \n",
       "9              0.719238                       426.0 2018-07-08 06:52:40+00:00   \n",
       "13             0.717773                       515.0 2018-07-08 06:30:54+00:00   \n",
       "2              0.716675                       497.0 2018-07-08 07:25:28+00:00   \n",
       "3              0.715942                       530.0 2018-07-08 07:25:56+00:00   \n",
       "17             0.714722                       440.0 2018-07-08 06:08:50+00:00   \n",
       "23             0.712402                       473.0 2018-07-08 05:39:19+00:00   \n",
       "4              0.709473                       425.0 2018-07-08 07:13:34+00:00   \n",
       "25             0.708130                       504.0 2018-07-08 05:29:14+00:00   \n",
       "20             0.704956                       493.0 2018-07-08 05:50:56+00:00   \n",
       "6              0.695679                       468.0 2018-07-08 07:03:36+00:00   \n",
       "5              0.690430                       516.0 2018-07-08 07:15:03+00:00   \n",
       "29             0.687378                       522.0 2018-07-08 05:08:11+00:00   \n",
       "21             0.658325                       615.0 2018-07-08 05:52:55+00:00   \n",
       "27             0.544189                       478.0 2018-07-08 05:17:47+00:00   \n",
       "19             0.417725                       374.0 2018-07-08 05:59:31+00:00   \n",
       "\n",
       "                             TrainingJobName TrainingJobStatus  \\\n",
       "12  sagemaker-mxnet-180708-0457-018-0672e23c         Completed   \n",
       "8   sagemaker-mxnet-180708-0457-022-602fe997         Completed   \n",
       "11  sagemaker-mxnet-180708-0457-019-510b97e9         Completed   \n",
       "14  sagemaker-mxnet-180708-0457-016-3c3a8322         Completed   \n",
       "7   sagemaker-mxnet-180708-0457-023-feac622d         Completed   \n",
       "0   sagemaker-mxnet-180708-0457-030-b96d5377         Completed   \n",
       "1   sagemaker-mxnet-180708-0457-029-25c7583d         Completed   \n",
       "28  sagemaker-mxnet-180708-0457-002-65adfb1f         Completed   \n",
       "22  sagemaker-mxnet-180708-0457-008-a1ecd004         Completed   \n",
       "15  sagemaker-mxnet-180708-0457-015-305bc5d4         Completed   \n",
       "18  sagemaker-mxnet-180708-0457-012-7dad3bd5         Completed   \n",
       "16  sagemaker-mxnet-180708-0457-014-031a27ff         Completed   \n",
       "26  sagemaker-mxnet-180708-0457-004-4cdcdee1         Completed   \n",
       "24  sagemaker-mxnet-180708-0457-006-8a90d72d         Completed   \n",
       "10  sagemaker-mxnet-180708-0457-020-8b0ac13f         Completed   \n",
       "9   sagemaker-mxnet-180708-0457-021-9752b883         Completed   \n",
       "13  sagemaker-mxnet-180708-0457-017-40ce6e41         Completed   \n",
       "2   sagemaker-mxnet-180708-0457-028-0fd8fcda         Completed   \n",
       "3   sagemaker-mxnet-180708-0457-027-99e8ad93         Completed   \n",
       "17  sagemaker-mxnet-180708-0457-013-9338d9e2         Completed   \n",
       "23  sagemaker-mxnet-180708-0457-007-8c6d6369         Completed   \n",
       "4   sagemaker-mxnet-180708-0457-026-d328bcb0         Completed   \n",
       "25  sagemaker-mxnet-180708-0457-005-6644667e         Completed   \n",
       "20  sagemaker-mxnet-180708-0457-010-b43d406a         Completed   \n",
       "6   sagemaker-mxnet-180708-0457-024-a1a9261a         Completed   \n",
       "5   sagemaker-mxnet-180708-0457-025-bd0557c9         Completed   \n",
       "29  sagemaker-mxnet-180708-0457-001-cb6d6b71         Completed   \n",
       "21  sagemaker-mxnet-180708-0457-009-f12240b7         Completed   \n",
       "27  sagemaker-mxnet-180708-0457-003-47aaa565         Completed   \n",
       "19  sagemaker-mxnet-180708-0457-011-492642e3         Completed   \n",
       "\n",
       "           TrainingStartTime  learning_rate      momentum        wd  \n",
       "12 2018-07-08 06:25:10+00:00       0.218998  7.807712e-01  0.000463  \n",
       "8  2018-07-08 06:45:14+00:00       0.491950  2.074798e-02  0.000330  \n",
       "11 2018-07-08 06:34:11+00:00       0.495178  1.530975e-02  0.000864  \n",
       "14 2018-07-08 06:14:44+00:00       0.394037  7.685146e-01  0.000663  \n",
       "7  2018-07-08 06:55:51+00:00       0.443138  6.272739e-01  0.000891  \n",
       "0  2018-07-08 07:28:13+00:00       0.095172  9.210969e-01  0.000408  \n",
       "1  2018-07-08 07:27:44+00:00       0.400276  5.187911e-02  0.000631  \n",
       "28 2018-07-08 04:59:24+00:00       0.308497  8.221134e-01  0.000838  \n",
       "22 2018-07-08 05:32:17+00:00       0.202243  7.614848e-01  0.000937  \n",
       "15 2018-07-08 06:11:25+00:00       0.399027  7.784146e-01  0.000653  \n",
       "18 2018-07-08 05:55:37+00:00       0.383464  1.314750e-02  0.000765  \n",
       "16 2018-07-08 06:04:57+00:00       0.499998  1.880845e-07  0.000024  \n",
       "26 2018-07-08 05:10:14+00:00       0.485280  8.946790e-01  0.000822  \n",
       "24 2018-07-08 05:21:07+00:00       0.453483  6.834462e-01  0.000155  \n",
       "10 2018-07-08 06:34:37+00:00       0.408935  1.748506e-01  0.000529  \n",
       "9  2018-07-08 06:45:34+00:00       0.323677  1.015553e-01  0.000299  \n",
       "13 2018-07-08 06:22:19+00:00       0.288515  8.637047e-01  0.000709  \n",
       "2  2018-07-08 07:17:11+00:00       0.210008  6.103743e-01  0.000493  \n",
       "3  2018-07-08 07:17:06+00:00       0.303875  8.004766e-01  0.000370  \n",
       "17 2018-07-08 06:01:30+00:00       0.449889  1.005885e-01  0.000002  \n",
       "23 2018-07-08 05:31:26+00:00       0.293129  0.000000e+00  0.000823  \n",
       "4  2018-07-08 07:06:29+00:00       0.347373  7.542925e-01  0.000085  \n",
       "25 2018-07-08 05:20:50+00:00       0.472519  5.562326e-01  0.000195  \n",
       "20 2018-07-08 05:42:43+00:00       0.138131  6.934573e-01  0.000000  \n",
       "6  2018-07-08 06:55:48+00:00       0.460894  6.460432e-01  0.000909  \n",
       "5  2018-07-08 07:06:27+00:00       0.392321  8.076493e-01  0.000025  \n",
       "29 2018-07-08 04:59:29+00:00       0.094162  5.240358e-01  0.000551  \n",
       "21 2018-07-08 05:42:40+00:00       0.285951  9.703842e-01  0.000000  \n",
       "27 2018-07-08 05:09:49+00:00       0.007274  3.568501e-01  0.000816  \n",
       "19 2018-07-08 05:53:17+00:00       0.460787  9.900000e-01  0.000985  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_metrics = sagemaker.HyperparameterTuningJobAnalytics(tuner._current_job_name).dataframe()\n",
    "bayes_metrics.sort_values(['FinalObjectiveValue'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at our results, we can see that, with one fourth the total training jobs, SageMaker's Automatic Model Tuning has produced a model with better accuracy 74% than our random search.  In addition, there's no guarantee that the effectiveness of random search wouldn't change over subsequent runs.\n",
    "\n",
    "Let's compare our hyperparameter's relationship to eachother and the objective metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(pd.concat([bayes_metrics[['FinalObjectiveValue',\n",
    "                                                     'learning_rate',\n",
    "                                                     'momentum',\n",
    "                                                     'wd']],\n",
    "                                      bayes_metrics['TrainingStartTime'].rank()],\n",
    "                           axis=1),\n",
    "                           figsize=(12, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that:\n",
    "* There's a range of reasonably good values for `learning_rate`, `momentum`, and `wd`, but there seem to be some very bad performers at both ends of the spectrum.\n",
    "* Later training jobs performed consistently better than early ones (SageMaker's Automatic Model Tuning was learning and effectively exploring the space).\n",
    "* There appears to be somewhat less of a random relationship between the hyperparameter values our meta-model approach tested.  This aligns with the knowledge that these hyperparameters are connected and that changing one can be offset by changing another.\n",
    "\n",
    "---\n",
    "\n",
    "## Wrap-up\n",
    "\n",
    "In this notebook, we saw the importance of hyperparameter tuning and discovered how much more effective Amazon SageMaker Automatic Model Tuning can be than random search.  We could extend this example by testing another brute force method like grid search, tuning additional hyperparameters, using this first round of hyperparameter tuning to inform a secondary round of  hyperparameter tuning where ranges have been narrowed down further, or applying this same comparison to your own problem.\n",
    "\n",
    "For more information on using SageMaker's Automatic Model Tuning, see our other [example notebooks](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/hyperparameter_tuning) and [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_mxnet_p27",
   "language": "python",
   "name": "conda_mxnet_p27"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  },
  "notice": "Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
